CN103810150A - Automatic relation nestable questionnaire generating method and device - Google Patents
Automatic relation nestable questionnaire generating method and device Download PDFInfo
- Publication number
- CN103810150A CN103810150A CN201210446503.2A CN201210446503A CN103810150A CN 103810150 A CN103810150 A CN 103810150A CN 201210446503 A CN201210446503 A CN 201210446503A CN 103810150 A CN103810150 A CN 103810150A
- Authority
- CN
- China
- Prior art keywords
- questionnaire
- relation
- nestable
- examination question
- basic data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an automatic relation nestable questionnaire generating method. The method comprises the following steps of logically extracting prototype template data from basic data, and reflecting the basic data by utilizing the prototype template data in an orderly layering logic structure way; determining a corresponding relation between a user group and a questionnaire and a questionnaire generation strategy; traversing and screening the prototype template data according to the determined corresponding relation between the user group and the questionnaire and the questionnaire generation strategy so as to generate the questionnaire. The invention also discloses an automatic relation nestable questionnaire generating device.
Description
Technical field
the present invention relates to data processing method, and more specifically, relate to the nestable questionnaire automatic generation method of a kind of relation and device.
Background technology
the generation of existing electronic examination question or questionnaire, is generally artificial layout.The examination question of this mode generates, normally disposal molding, and for different colonies or different scene, just reusable not of original examination question and questionnaire.Under this kind of situation, in order to meet examination question audient's the demand of next round, need the new examination question collection of layout or questionnaire collection, it is extremely huge that artificial workload just seems, greatly reduces the efficiency of work.
application number be 200410062414.3 Chinese patent application disclosed " adopting the Item Bank in Auto-generating Paper Based implementation method of improved genetic algorithms method " shown a kind of according to user's collocation strategy, automatically generate the method for test item bank based on genetic algorithm.Specifically, the method adopts improved genetic algorithms method to realize the automatic volume group of test item bank, comprising following operation steps: (1) formulates tactic of generating test paper---the examination question distributed intelligence providing according to test item bank produces human-computer interaction interface, and nationality is worked out the strategy of a set of automatic volume group by this interface user; Described tactic of generating test paper is exactly standard and the constraint condition that the attribute of all examination questions in target paper is proposed; (2) choose examination question---the examination question screening conditions that form according to described tactic of generating test paper are chosen examination question from test item bank, and the property parameters of these selected examination questions will be used to genetic algorithm automatic volume group; (3) automatic volume group---form fitness function according to described tactic of generating test paper, then according to every a paper, be chromosome or individual fitness function value and improve after genetic algorithm carry out the genetic screening of examination question colony, until select one group of examination question composition paper that fitness function value is the highest.
this application, according to examination question attribute, carrys out to generate to property at random and preferentially paper by computerized algorithm, stresses to solve how to make the random paper generating meet distribution and the control etc. of the person of setting a question to Degree of difficulty of test paper by algorithm control.But, the automatic volume group method that this application provides does not consider examination question to carry out logical division, make the method can not be used for solving and between examination question and examination question, exist the questionnaire of causal relation (being called again in this article nest relation) to generate, be i.e. the automatic generation of the nestable questionnaire of relation.
Summary of the invention
for these problems, the present invention proposes the nestable questionnaire automatic generation method of a kind of relation, comprise: from basic data, logic extracts blank template data, described blank template data reflects described basic data in the mode of orderly level logical organization; Determine corresponding relation and questionnaire generation strategy between user's group and questionnaire; And according to the corresponding relation between determined user's group and questionnaire and questionnaire generation strategy, described blank template data is traveled through screening and generates questionnaire.
in the nestable questionnaire automatic generation method of above-mentioned relation, described basic data is a set of amorphous exercise question at random storehouse.
in the nestable questionnaire automatic generation method of above-mentioned relation, the described step that logic extracts blank template data from basic data comprises:
a) according to questionnaire characteristic, described basic data is assembled to classification;
b) basic data of assembling after sorting out is carried out to level cataloguing;
c) logical relation of assembling between the basic data after sorting out is carried out pre-stored and nested logic is degraded; And
d) basic data of assembling after sorting out is carried out extensive encapsulation and generated blank template data, make described blank template packet containing the level cataloguing of described basic data and the information of logical relation.
in the nestable questionnaire automatic generation method of above-mentioned relation, basic data after overbunching is sorted out reflects with tree structure, wherein the root node in tree structure represents independently questionnaire of portion, under described root node, there is the some different crosshead comprising as under the some different father's topic comprising under the some different chapters and sections that split into again according to exercise question character of minor matters point or leaf node, different chapters and sections and father's topic, between described father's topic and described crosshead, there is nest relation.
in the nestable questionnaire automatic generation method of above-mentioned relation, describedly carry out level cataloguing and comprise the tree structure questionnaire to having organized assembling basic data after sorting out, complete the cataloguing from root node to leaf node, wherein the cataloguing of each examination question node by by traversal approach the node ID of process be spliced.
in the nestable questionnaire automatic generation method of above-mentioned relation, describedly carry out pre-stored and be included in the father that each examination question Nodes records its level of living in numbering and last layer level and inscribe numbering assembling logical relation between the basic data after sorting out.
in the nestable questionnaire automatic generation method of above-mentioned relation, described blank template packet is drawn together at least one in following content: whether whether examination question numbering, father inscribe numbering, level numbering, examination question stem, topic type numbering, option value, trigger value, degree-of-difficulty factor, must answer and issue.
in the nestable questionnaire automatic generation method of above-mentioned relation, corresponding relation and questionnaire generation strategy between described definite user's group and questionnaire comprise: organize characteristic according to the user who takes out, corresponding relation and questionnaire generation strategy between user's group and questionnaire are set.
in the nestable questionnaire automatic generation method of above-mentioned relation, described user organizes characteristic and comprises user's sex, occupation, education degree, institutional affiliation classification, institutional affiliation character, the type of commencing business.
in the nestable questionnaire automatic generation method of above-mentioned relation, described questionnaire generation strategy comprises mode and from bottom to top mode from top to bottom, described mode from top to bottom refers to start to travel through out corresponding questionnaire and examination question collection example according to questionnaire tree structure tissue from root node, and described mode from bottom to top refers to upwards review the instantiation of examination question collection according to the attribute of examination question from leaf node.
the nestable questionnaire automatic generation method of above-mentioned relation also can comprise: by receiving the examination question parameter input of questionnaire example, complete the Dynamic Display of real-time loading and the nest relation of questionnaire.
the nestable questionnaire automatic generation method of above-mentioned relation also can comprise: user's logging data is landed to processing.
according to another embodiment of the invention, a kind of device automatically generating for the nestable questionnaire of relation is provided, comprise: logic extracting unit, described logic extracting unit is for extracting blank template data from basic data logic, and wherein said blank template data reflects described basic data in the mode of orderly level logical organization; Strategy dispensing unit, described tactful dispensing unit is for determining corresponding relation and the questionnaire generation strategy between user's group and questionnaire; And questionnaire factory, described questionnaire factory, for according to the corresponding relation between determined user's group and questionnaire and questionnaire generation strategy, travel through screening and generation questionnaire to described blank template data.
at the above-mentioned device automatically generating for the nestable questionnaire of relation, described basic data is a set of amorphous exercise question at random storehouse.
at the above-mentioned device automatically generating for the nestable questionnaire of relation, described logic extracting unit further comprises: grouping and classifying module, for described basic data being assembled to classification according to questionnaire characteristic; Level Catalogue Module, for carrying out level cataloguing to the basic data of assembling after sorting out; Peel off nested module, for the logical relation of assembling between the basic data after sorting out is carried out to pre-stored; And extensive package module, for the basic data of assembling after sorting out is carried out extensive encapsulation and generated blank template data, make described blank template packet containing the level cataloguing of described basic data and the information of logical relation.
at the above-mentioned device automatically generating for the nestable questionnaire of relation, basic data after overbunching is sorted out reflects with tree structure, wherein the root node in tree structure represents independently questionnaire of portion, under described root node, there is the some different crosshead comprising as under the some different father's topic comprising under the some different chapters and sections that split into again according to exercise question character of minor matters point or leaf node, different chapters and sections and father's topic, between described father's topic and described crosshead, there is nest relation.
at the above-mentioned device automatically generating for the nestable questionnaire of relation, described level Catalogue Module completes the cataloguing from root node to leaf node to the tree structure questionnaire of having organized, wherein the cataloguing of each examination question node by by traversal approach the node ID of process be spliced.
at the above-mentioned device automatically generating for the nestable questionnaire of relation, described in peel off the father that nested module records its level numbering of living in and last layer level at each examination question Nodes and inscribe numbering.
at the above-mentioned device automatically generating for the nestable questionnaire of relation, described blank template packet is drawn together at least one in following content: whether whether examination question numbering, father inscribe numbering, level numbering, examination question stem, topic type numbering, option value, trigger value, degree-of-difficulty factor, must answer and issue.
at the above-mentioned device automatically generating for the nestable questionnaire of relation, described tactful dispensing unit is organized characteristic according to the user who takes out, and corresponding relation and questionnaire generation strategy between user's group and questionnaire are set.
at the above-mentioned device for the nestable questionnaire automatic generation method of relation, described user organizes characteristic and comprises user's sex, occupation, education degree, institutional affiliation classification, institutional affiliation character, the type of commencing business.
at the above-mentioned device automatically generating for the nestable questionnaire of relation, described questionnaire generation strategy comprises mode and from bottom to top mode from top to bottom, described mode from top to bottom refers to start to travel through out corresponding questionnaire and examination question collection example according to questionnaire tree structure tissue from root node, and described mode from bottom to top refers to upwards review the instantiation of examination question collection according to the attribute of examination question from leaf node.
the above-mentioned device automatically generating for the nestable questionnaire of relation also can comprise: display unit, described display unit, for the examination question parameter input by receiving questionnaire example, completes the Dynamic Display of real-time loading and the nest relation of questionnaire.
the above-mentioned device automatically generating for the nestable questionnaire of relation also can comprise: data analysis unit, described data analysis unit is for landing processing to user's logging data.
Accompanying drawing explanation
after having read the specific embodiment of the present invention with reference to accompanying drawing, those skilled in the art will become apparent various aspects of the present invention.Those skilled in the art should be understood that: these accompanying drawings are only for coordinating embodiment that technical scheme of the present invention is described, and are not intended to protection scope of the present invention to be construed as limiting.
fig. 1 be according to one embodiment of present invention, the process flow diagram of the nestable questionnaire automatic generation method of relation;
fig. 2 be according to one embodiment of present invention, the structural representation of the device that automatically generates for the nestable questionnaire of relation;
fig. 3 be according to one embodiment of present invention, with the schematic diagram of tree structure display base data;
fig. 4 is the structural representation of logic extracting unit according to an embodiment of the invention.
Embodiment
what introduce below is some in multiple possibility embodiment of the present invention, aims to provide basic understanding of the present invention, is not intended to confirm key of the present invention or conclusive key element or limits claimed scope.Easily understand, according to technical scheme of the present invention, do not changing under connotation of the present invention other implementation that one of ordinary skill in the art can propose mutually to replace.Therefore, below embodiment and accompanying drawing be only the exemplary illustration to technical scheme of the present invention, and should not be considered as of the present invention all or be considered as restriction or the restriction to technical solution of the present invention.
along with the development of modern network technology, electric network education, the development of the business such as electronic information investigation, the questionnaire of robotization generates, the questionnaire of robotization generates and organizing by hand the human cost on exam pool and questionnaire by greatly saving.The questionnaire that the invention provides a kind of robotization generates method and apparatus, puts forth effort to solve the questionnaire Generating Problems in the time existing causal relation to be nest relation between examination question and examination question.
with reference to figure 1, it shows according to one embodiment of present invention, the process flow diagram of the nestable questionnaire automatic generation method 100 of relation.
in step 110, from basic data, logic extracts blank template data, and wherein blank template data reflects basic data in the mode of orderly level logical organization.Conventionally, basic data is a set of amorphous exercise question at random storehouse.
for the nestable robotization questionnaire of implementation relation generates, in one embodiment, can in the following manner unordered scattered exam pool be assembled into orderly level logical organization.Concrete steps are as follows:
a) grouping and classifying.According to questionnaire characteristic, scattered exercise question is assembled to classification, similar examination question can be returned to same questionnaire set, and questionnaire can split different little chapters and sections again according to exercise question character, forms a tree, as shown in Figure 3.Root node is an independently questionnaire, is discrete each other between questionnaire and questionnaire.Questionnaire set is whole exam pool set.Leaf node in tree must be examination question, but due to the existence of nested logic, be not that each examination question one establishes a capital is leaf node.B) level cataloguing.To the tree questionnaire of having organized, complete the cataloguing from root node to leaf node.The object of cataloguing is to improve robotization and generates the traversal effectiveness of retrieval in questionnaire process.The cataloguing of each examination question node be exactly by traversal approach the node ID of process be spliced.For example root node sequence number A01, chapters and sections sequence number 01 under this node, exercise question sequence number 001 under these chapters and sections, this questionnaire storage numbering A01, chapters and sections storage numbering A0101, examination question storage numbering is A0101001.The tree logical relation of questionnaire-chapters and sections-examination question can be stored by relevant database 1-n relation.C) peel off nested.In order to solve the displaying that has cause-effect relationship examination question, the logical relation between examination question can be carried out to pre-stored.This nested logic is a kind of tree tissue that can recurrence.Under father's topic, there is crosshead, under crosshead, have crosshead, in logic by that analogy.The storage difficult point of bringing in order to evade recurrence logic, preferably, can consider to take the mode of individual layer storage that multilayer nest relation is decomposed and peeled off, and simplifies storage process.Be that the father that each examination question only records level of living in numbering and last layer level inscribes ID.If this topic is inscribed without father, his father inscribes ID and can be labeled as a certain special value as 00000000, to indicate this to be entitled as top father's topic.D) extensive encapsulation.Between examination question, be all different, each examination question is a special entity.For example exercise question trunk information difference, topic type is also different, multiple-choice question, fills a vacancy, discusses etc.The existence of singularity must bring difficulty to versatility.Realize the robotization of questionnaire assembling, just need to shield singularity and improve versatility.Therefore can extract the general character of examination question, each examination question is carried out to unified encapsulation, complete the extensive process of singularity to ubiquity.In one embodiment, blank template data can include but not limited to following content:
wherein, topic type numbering can cover all topic types, including, but not limited to: fill a vacancy topic, digital space-filling topic, discussion topic, file of true and false, single choice, multiple choice, text uploaded topic, parameter single choice, parameter multiple choice, date multiple-choice question etc.Option value comes into force conventionally in the time that topic type is multiple-choice question.For storing every selective value of multiple-choice question, between each option, can be distinguished by certain special separator, as " option1|option2|option3 ".Option value can be decomposed according to separator, thereby is shown as neatly the different options of examination question.In addition, trigger value comes into force conventionally in the time being originally entitled as crosshead, a necessary condition while being triggered demonstration for storing this crosshead, i.e. certain option value of upper level father topic.The for example answer of his father's topic has been chosen to option1 by user, and this crosshead will be triggered in logic, and this crosshead is in extensive encapsulation process, and trigger value just should be set to option1.In addition, whether must answer the control for completing the mandatory input of the page.
the questionnaire various information becoming based on above step break-up tissue is referred to as blank template data, and collaborative its lower each node data of each independently questionnaire is referred to as questionnaire template.
in step 120, determine corresponding relation and questionnaire generation strategy between user's group and questionnaire.In a specific embodiment, can organize characteristic by taking out user, corresponding relation and questionnaire generation strategy between user's group and questionnaire are set.User organizes characteristic including, but not limited to user's sex, occupation, education degree, institutional affiliation classification, institutional affiliation character, the type etc. of commencing business.Different users organizes corresponding different questionnaire set.Questionnaire generation strategy can be from top to bottom, can be also from bottom to top.Refer to from top to bottom according to questionnaire tree tissue and start to travel through out corresponding questionnaire and examination question collection example from root node, carry out examination question collection example according to the attribute of examination question from examination question granularity and refer to from bottom to top, upwards reviewed questionnaire assembling from leaf node.
in step 130, according to the corresponding relation between determined user's group and questionnaire and questionnaire generation strategy, blank template data is traveled through screening and generates questionnaire.Extract each generic attribute of questionnaire and examination question by logic, based on each attributes match user personality, make different login users see to such an extent that be the different questionnaires that conform to from self-characteristic, thereby complete the scene-type customization of questionnaire.
alternatively, as shown in the step 140 of Fig. 1, the nestable questionnaire automatic generation method of relation also can comprise the examination question parameter input by receiving questionnaire example, completes the Dynamic Display of real-time loading and the nest relation of questionnaire.Further, in step 150, user's logging data is for example landed, to the processing of storage system (database).
the nestable questionnaire automatic generation method of above-mentioned relation can successfully discharge the repeated works such as a large amount of page developments, has improved Service control ability and the adaptability to changes of questionnaire.
according to another aspect of the present invention, provide a kind of device 200 automatically generating for the nestable questionnaire of relation.Device 200 can comprise: logic extracting unit 220, and for extracting blank template data from basic data logic, wherein said blank template data reflects described basic data in the mode of orderly level logical organization; Strategy dispensing unit 210, for determining corresponding relation and the questionnaire generation strategy between user's group and questionnaire; And questionnaire factory 230, described questionnaire factory, for according to the corresponding relation between determined user's group and questionnaire and questionnaire generation strategy, travel through screening and generation questionnaire to described blank template data.
in one embodiment, as shown in Figure 4, logic extracting unit 220 can comprise grouping and classifying module 222, level Catalogue Module 224, peel off nested module 226 and extensive package module 228.
grouping and classifying module 222 can be assembled classification by scattered exercise question according to questionnaire characteristic, and similar examination question can be returned to same questionnaire set, and questionnaire can split different little chapters and sections again according to exercise question character, forms a tree.Root node is an independently questionnaire, is discrete each other between questionnaire and questionnaire.Questionnaire set is whole exam pool set.Leaf node in tree must be examination question, but due to the existence of nested logic, be not that each examination question one establishes a capital is leaf node.
in order to solve the displaying that has cause-effect relationship examination question, peel off nested module 226 and the logical relation between examination question can be carried out to pre-stored.This nested logic is a kind of tree tissue that can recurrence.Under father's topic, there is crosshead, under crosshead, have crosshead, in logic by that analogy.The storage difficult point of bringing in order to evade recurrence logic, peels off nested module 226 and takes the mode of individual layer storage that multilayer nest relation is decomposed and peeled off, and simplifies storage process.Be that the father that each examination question only records level of living in numbering and last layer level inscribes ID.If this topic is inscribed without father, his father inscribes ID and can be labeled as a certain special value as 00000000, to indicate this to be entitled as top father's topic.
wherein, topic type numbering can cover all topic types, including, but not limited to: fill a vacancy topic, digital space-filling topic, discussion topic, file of true and false, single choice, multiple choice, text uploaded topic, parameter single choice, parameter multiple choice, date multiple-choice question etc.Option value comes into force conventionally in the time that topic type is multiple-choice question.For storing every selective value of multiple-choice question, between each option, can be distinguished by certain special separator, as " option1|option2|option3 ".Option value can be decomposed according to separator, thereby is shown as neatly the different options of examination question.In addition, trigger value comes into force conventionally in the time being originally entitled as crosshead, a necessary condition while being triggered demonstration for storing this crosshead, i.e. certain option value of upper level father topic.The for example answer of his father's topic has been chosen to option1 by user, and this crosshead will be triggered in logic, and this crosshead is in extensive encapsulation process, and trigger value just should be set to option1.In addition, whether must answer the control for completing the mandatory input of the page.
in one embodiment, tactful dispensing unit 210 takes out user and organizes characteristic, and corresponding relation and questionnaire generation strategy between user's group and questionnaire are set.User organizes characteristic including, but not limited to user's sex, occupation, education degree, institutional affiliation classification, institutional affiliation character, the type etc. of commencing business.Different users organizes corresponding different questionnaire set.Questionnaire generation strategy can be from top to bottom, can be also from bottom to top.Mode refers to according to questionnaire tree tissue and starts to travel through out corresponding questionnaire and examination question collection example from root node from top to bottom; Mode refers to from bottom to top, carries out examination question collection example according to the attribute of examination question from examination question granularity, has upwards reviewed questionnaire assembling from leaf node.
in yet another embodiment, questionnaire factory 230 is workshops of questionnaire instantiation.Questionnaire factory 230 completes from tactful dispensing unit 210 and extracts processing instruction (being corresponding relation, the questionnaire generation strategy of user's group-questionnaire), from blank template data, the qualified questionnaire of traversal screening carries out scene-type assembling, the questionnaire example generating is to derive from questionnaire template according to specific user's scene, brought the questionnaire of particular group of users branding, different user's groups has different questionnaire examples.Questionnaire example is the subset of questionnaire template normally, and the tree tissue that questionnaire example is corresponding is the subtree of questionnaire template tree tissue.
device 200 can further comprise display unit 240 and Data Analysis unit 250.Display unit 240 is by receiving the examination question parameter input of questionnaire example, and the recursive call page is shown instruction set, completes the Dynamic Display of real-time loading and the nest relation of questionnaire.Display unit 240 can be safeguarded a set of complete topic type page displaying instruction set, and this instruction set covers all possible topic type of examination question and shows.For example multiple-choice question, its page of the topic of filling a vacancy are shown just will present diverse page form.Display unit 240 obtains the bottommost layer level in the corresponding tree tissue of questionnaire example, shows instruction set according to the level number of cycles output page, questionnaire example is traveled through simultaneously.In displaying process, each attribute of extensive packaged examination question will pass to display unit 240 as parameter.240 inputs of the parameter according to examination question of display unit, select suitable topic type to carry out differentiation displaying and the input control (whether must answer) etc. of web page form.In addition, display unit 240 also can obtain the script that user inserts, and by answer and the contrast of crosshead trigger value, corresponding crosshead is shown, otherwise will not show if met trigger mechanism.Thereby complete the displaying of father's topic and the causal analysis of crosshead and complicated nest relation.In addition, questionnaire and chapters and sections entity can be transparent or opaque to user, can allow user see the structure of questionnaire-chapters and sections-examination question, also can shield this structure, only see the big collection of an examination question.After questionnaire is submitted to, data are paid data analysis unit 250 by display unit 240.
as there being again subsequent analysis, only need to from storage system, extract instance data and process.
it is nested that said apparatus 200 can be realized the exercise question of multilayer logic relation, can realize the different answers based on a upper topic time, hides or show next different topic time, complete the function of the nested and conversion of exercise question cause and effect in logic.Thereby solve the group volume restriction generally bringing without logicality questionnaire.
from economy, adopt apparatus and method of the present invention can make user reduce tissue and change various questionnaire, the work of examination question set, thereby reduce artificial expense, avoid the overlapping development bringing because of the variation of business demand.From social benefit, apparatus and method of the present invention on the e-survey questionnaire robotization of financial circles or other industry is processed and the aspect such as educational business electronization education examination have high practicality.
above, describe the specific embodiment of the present invention with reference to the accompanying drawings.But those skilled in the art can understand, without departing from the spirit and scope of the present invention in the situation that, can also do various changes and replacement to the specific embodiment of the present invention.These changes and replacement all drop in the claims in the present invention book limited range.
Claims (24)
1. the nestable questionnaire automatic generation method of relation, comprising:
From basic data, logic extracts blank template data, and described blank template data reflects described basic data in the mode of orderly level logical organization;
Determine corresponding relation and questionnaire generation strategy between user's group and questionnaire; And
According to the corresponding relation between determined user's group and questionnaire and questionnaire generation strategy, described blank template data is traveled through screening and generates questionnaire.
2. the nestable questionnaire automatic generation method of relation as claimed in claim 1, wherein, described basic data is a set of amorphous exercise question at random storehouse.
3. the nestable questionnaire automatic generation method of relation as claimed in claim 1, wherein, the described step that logic extracts blank template data from basic data comprises:
A) according to questionnaire characteristic, described basic data is assembled to classification;
B) basic data of assembling after sorting out is carried out to level cataloguing;
C) logical relation of assembling between the basic data after sorting out is carried out to pre-stored; And
D) basic data of assembling after sorting out is carried out extensive encapsulation and generated blank template data, make described blank template packet containing the level cataloguing of described basic data and the information of logical relation.
4. the nestable questionnaire automatic generation method of relation as claimed in claim 3, wherein, basic data after overbunching is sorted out reflects with tree structure, wherein the root node in tree structure represents independently questionnaire of portion, under described root node, there is the some different crosshead comprising as under the some different father's topic comprising under the some different chapters and sections that split into again according to exercise question character of minor matters point or leaf node, different chapters and sections and father's topic, between described father's topic and described crosshead, there is nest relation.
5. the nestable questionnaire automatic generation method of the relation as described in claim 3 or 4, wherein, describedly carry out level cataloguing and comprise the tree structure questionnaire to having organized assembling basic data after sorting out, complete the cataloguing from root node to leaf node, wherein the cataloguing of each examination question node by by traversal approach the node ID of process be spliced.
6. the nestable questionnaire automatic generation method of the relation as described in claim 3 or 4, wherein, describedly carry out pre-stored and be included in the father that each examination question Nodes records its level of living in numbering and last layer level and inscribe numbering assembling logical relation between the basic data after sorting out.
7. the nestable questionnaire automatic generation method of relation as claimed in claim 3, wherein, described blank template packet is drawn together at least one in following content: whether whether examination question numbering, father inscribe numbering, level numbering, examination question stem, topic type numbering, option value, trigger value, degree-of-difficulty factor, must answer and issue.
8. the nestable questionnaire automatic generation method of relation as claimed in claim 1, wherein, corresponding relation and questionnaire generation strategy between described definite user's group and questionnaire comprise: organize characteristic according to the user who takes out, corresponding relation and questionnaire generation strategy between user's group and questionnaire are set.
9. the nestable questionnaire automatic generation method of relation as claimed in claim 8, wherein, described user organizes characteristic and comprises user's sex, occupation, education degree, institutional affiliation classification, institutional affiliation character, the type of commencing business.
10. the nestable questionnaire automatic generation method of relation as claimed in claim 8, wherein, described questionnaire generation strategy comprises mode and from bottom to top mode from top to bottom, described mode from top to bottom refers to start to travel through out corresponding questionnaire and examination question collection example according to questionnaire tree structure tissue from root node, and described mode from bottom to top refers to upwards review the instantiation of examination question collection according to the attribute of examination question from leaf node.
The nestable questionnaire automatic generation method of 11. relation as claimed in claim 1, also comprises: by receiving the examination question parameter input of questionnaire example, complete the Dynamic Display of real-time loading and the nest relation of questionnaire.
The nestable questionnaire automatic generation method of 12. relation as claimed in claim 1, also comprises: user's logging data is landed to processing.
13. 1 kinds of devices that automatically generate for the nestable questionnaire of relation, comprising:
Logic extracting unit, described logic extracting unit is for extracting blank template data from basic data logic, and wherein said blank template data reflects described basic data in the mode of orderly level logical organization;
Strategy dispensing unit, described tactful dispensing unit is for determining corresponding relation and the questionnaire generation strategy between user's group and questionnaire; And
Questionnaire factory, described questionnaire factory, for according to the corresponding relation between determined user's group and questionnaire and questionnaire generation strategy, travel through screening and generation questionnaire to described blank template data.
14. devices that automatically generate for the nestable questionnaire of relation as claimed in claim 13, wherein, described basic data is a set of amorphous exercise question at random storehouse.
15. devices that automatically generate for the nestable questionnaire of relation as claimed in claim 13, wherein, described logic extracting unit further comprises:
A) grouping and classifying module, for assembling classification according to questionnaire characteristic by described basic data;
B) level Catalogue Module, for carrying out level cataloguing to the basic data of assembling after sorting out;
C) peel off nested module, for the logical relation of assembling between the basic data after sorting out is carried out pre-stored and nested logic is degraded; And
D) extensive package module, for the basic data of assembling after sorting out is carried out extensive encapsulation and generated blank template data, makes described blank template packet containing the level cataloguing of described basic data and the information of logical relation.
16. devices that automatically generate for the nestable questionnaire of relation as claimed in claim 15, wherein, basic data after overbunching is sorted out reflects with tree structure, wherein the root node in tree structure represents independently questionnaire of portion, under described root node, there is the some different crosshead comprising as under the some different father's topic comprising under the some different chapters and sections that split into again according to exercise question character of minor matters point or leaf node, different chapters and sections and father's topic, between described father's topic and described crosshead, there is nest relation.
17. devices that automatically generate for the nestable questionnaire of relation as described in claim 15 or 16, wherein, described level Catalogue Module completes the cataloguing from root node to leaf node to the tree structure questionnaire of having organized, wherein the cataloguing of each examination question node by by traversal approach the node ID of process be spliced.
18. devices that automatically generate for the nestable questionnaire of relation as described in claim 15 or 16, wherein, described in peel off the father that nested module records its level numbering of living in and last layer level at each examination question Nodes and inscribe numbering.
19. devices that automatically generate for the nestable questionnaire of relation as claimed in claim 15, wherein, described blank template packet is drawn together at least one in following content: whether whether examination question numbering, father inscribe numbering, level numbering, examination question stem, topic type numbering, option value, trigger value, degree-of-difficulty factor, must answer and issue.
20. devices that automatically generate for the nestable questionnaire of relation as claimed in claim 13, wherein, described tactful dispensing unit is organized characteristic according to the user who takes out, and corresponding relation and questionnaire generation strategy between user's group and questionnaire are set.
21. devices for the nestable questionnaire automatic generation method of relation as claimed in claim 20, wherein, described user organizes characteristic and comprises user's sex, occupation, education degree, institutional affiliation classification, institutional affiliation character, the type of commencing business.
22. devices that automatically generate for the nestable questionnaire of relation as claimed in claim 20, wherein, described questionnaire generation strategy comprises mode and from bottom to top mode from top to bottom, described mode from top to bottom refers to start to travel through out corresponding questionnaire and examination question collection example according to questionnaire tree structure tissue from root node, and described mode from bottom to top refers to upwards review the instantiation of examination question collection according to the attribute of examination question from leaf node.
23. devices that automatically generate for the nestable questionnaire of relation as claimed in claim 13, also comprise: display unit, for the examination question parameter input by receiving questionnaire example, completes the Dynamic Display of real-time loading and the nest relation of questionnaire.
24. devices that automatically generate for the nestable questionnaire of relation as claimed in claim 13, also comprise: data analysis unit, and for user's logging data being landed to processing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210446503.2A CN103810150A (en) | 2012-11-09 | 2012-11-09 | Automatic relation nestable questionnaire generating method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210446503.2A CN103810150A (en) | 2012-11-09 | 2012-11-09 | Automatic relation nestable questionnaire generating method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103810150A true CN103810150A (en) | 2014-05-21 |
Family
ID=50706937
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210446503.2A Pending CN103810150A (en) | 2012-11-09 | 2012-11-09 | Automatic relation nestable questionnaire generating method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103810150A (en) |
Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104331392A (en) * | 2014-11-21 | 2015-02-04 | 北京金和软件股份有限公司 | Method capable of editing display content in image-text APP (application) in batch |
| CN104572136A (en) * | 2015-02-15 | 2015-04-29 | 奚峰 | Graphical questionnaire generation method and graphical questionnaire generation system |
| CN104699973A (en) * | 2015-03-19 | 2015-06-10 | 腾讯科技（深圳）有限公司 | Method and device for controlling logic of questionnaires |
| CN105068980A (en) * | 2015-08-07 | 2015-11-18 | 北京思特奇信息技术股份有限公司 | Graphical display method and system for jump relationship among topics |
| CN105824918A (en) * | 2016-03-16 | 2016-08-03 | 平安科技（深圳）有限公司 | Method for generating questionnaire and terminal |
| CN105956094A (en) * | 2016-04-29 | 2016-09-21 | 杭州本构科技有限公司 | Constructing method and publishing system of complex interactive content |
| CN106407446A (en) * | 2016-09-29 | 2017-02-15 | 中科易研（北京）科技股份公司 | Network questionnaire establishment method and device |
| CN106682929A (en) * | 2015-11-10 | 2017-05-17 | 北京国双科技有限公司 | Information analysis method and device |
| CN106933790A (en) * | 2017-03-02 | 2017-07-07 | 重庆砖家宝网络科技发展有限公司 | Contract template preparation method and system |
| CN107169274A (en) * | 2017-05-05 | 2017-09-15 | 深圳市心智心理测量技术研究所有限公司 | A Si Burger syndrome questionary handles method, device, equipment and storage medium |
| CN108074140A (en) * | 2018-02-09 | 2018-05-25 | 弘成科技发展有限公司 | Intelligent Questionnaire systems and collecting method |
| CN108229805A (en) * | 2017-12-27 | 2018-06-29 | 苏州工业园区报关有限公司 | Rule dynamic evaluating system and its method are closed in trade |
| CN109003117A (en) * | 2018-06-14 | 2018-12-14 | 万翼科技有限公司 | Generation method, device and the computer readable storage medium of questionnaire |
| CN109087129A (en) * | 2018-07-11 | 2018-12-25 | 万翼科技有限公司 | User's evaluation method, apparatus and computer readable storage medium |
| CN109165276A (en) * | 2018-08-22 | 2019-01-08 | 云图元睿（上海）科技有限公司 | Self-service questionnaire logic design method and system based on natural language |
| CN109344246A (en) * | 2018-09-25 | 2019-02-15 | 平安科技（深圳）有限公司 | A kind of electric questionnaire generation method, computer readable storage medium and terminal device |
| CN109598553A (en) * | 2018-12-04 | 2019-04-09 | 云图元睿（上海）科技有限公司 | Questionnaire intelligently tears conjunction method and system open |
| CN110189802A (en) * | 2019-04-28 | 2019-08-30 | 万达信息股份有限公司 | Biaxial stress structure cohort study information system based on index storage model |
| CN110362791A (en) * | 2019-02-22 | 2019-10-22 | 裴信 | Processing method, device and the computer readable storage medium of logic relevant issues |
| CN110517021A (en) * | 2019-08-27 | 2019-11-29 | 出门问问信息科技有限公司 | A kind of data processing method, device, storage medium and electronic equipment |
| CN110752037A (en) * | 2019-10-23 | 2020-02-04 | 泰康保险集团股份有限公司 | Evaluation method and device, computer readable medium and electronic device |
| CN111369290A (en) * | 2020-03-05 | 2020-07-03 | 广州快决测信息科技有限公司 | Method and system for automatically generating data acquisition module |
| CN111370082A (en) * | 2018-12-26 | 2020-07-03 | 医渡云（北京）技术有限公司 | Question hiding display processing method and device, electronic equipment and readable medium |
| CN111460768A (en) * | 2019-01-02 | 2020-07-28 | 中国移动通信有限公司研究院 | Questionnaire processing method and device, electronic equipment and storage medium |
| CN113033158A (en) * | 2021-03-20 | 2021-06-25 | 广州快决测信息科技有限公司 | Questionnaire display method, device and storage medium |
| CN113674032A (en) * | 2021-08-30 | 2021-11-19 | 广州快决测信息科技有限公司 | Net recommendation value questionnaire model building system and method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020119433A1 (en) * | 2000-12-15 | 2002-08-29 | Callender Thomas J. | Process and system for creating and administering interview or test |
| CN1588308A (en) * | 2004-07-02 | 2005-03-02 | 北京邮电大学 | Method for realizing automatically compiling test paper from item pool using improved genetic calculation |
| CN101470727A (en) * | 2007-12-24 | 2009-07-01 | 新奥特（北京）视频技术有限公司 | Method and system for editing and processing tree-form data |
| CN101650723A (en) * | 2009-09-16 | 2010-02-17 | 南京联创科技集团股份有限公司 | Tariff template tree setting method in charging account engine |
| US20100179962A1 (en) * | 2005-12-15 | 2010-07-15 | Simpliance, Inc. | Methods and Systems for Intelligent Form-Filling and Electronic Document Generation |
-
2012
- 2012-11-09 CN CN201210446503.2A patent/CN103810150A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020119433A1 (en) * | 2000-12-15 | 2002-08-29 | Callender Thomas J. | Process and system for creating and administering interview or test |
| CN1588308A (en) * | 2004-07-02 | 2005-03-02 | 北京邮电大学 | Method for realizing automatically compiling test paper from item pool using improved genetic calculation |
| US20100179962A1 (en) * | 2005-12-15 | 2010-07-15 | Simpliance, Inc. | Methods and Systems for Intelligent Form-Filling and Electronic Document Generation |
| CN101470727A (en) * | 2007-12-24 | 2009-07-01 | 新奥特（北京）视频技术有限公司 | Method and system for editing and processing tree-form data |
| CN101650723A (en) * | 2009-09-16 | 2010-02-17 | 南京联创科技集团股份有限公司 | Tariff template tree setting method in charging account engine |
Cited By (39)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104331392B (en) * | 2014-11-21 | 2017-06-27 | 北京金和软件股份有限公司 | A kind of method that can in batches edit displaying content in picture and text APP |
| CN104331392A (en) * | 2014-11-21 | 2015-02-04 | 北京金和软件股份有限公司 | Method capable of editing display content in image-text APP (application) in batch |
| CN104572136A (en) * | 2015-02-15 | 2015-04-29 | 奚峰 | Graphical questionnaire generation method and graphical questionnaire generation system |
| CN104572136B (en) * | 2015-02-15 | 2018-02-23 | 奚峰 | The graphical generation method and system of questionnaire |
| CN104699973A (en) * | 2015-03-19 | 2015-06-10 | 腾讯科技（深圳）有限公司 | Method and device for controlling logic of questionnaires |
| CN105068980A (en) * | 2015-08-07 | 2015-11-18 | 北京思特奇信息技术股份有限公司 | Graphical display method and system for jump relationship among topics |
| CN106682929B (en) * | 2015-11-10 | 2021-01-22 | 北京国双科技有限公司 | Information analysis method and device |
| CN106682929A (en) * | 2015-11-10 | 2017-05-17 | 北京国双科技有限公司 | Information analysis method and device |
| CN105824918A (en) * | 2016-03-16 | 2016-08-03 | 平安科技（深圳）有限公司 | Method for generating questionnaire and terminal |
| CN105956094B (en) * | 2016-04-29 | 2019-12-20 | 杭州本构科技有限公司 | Construction method and release system of complex interactive content |
| CN105956094A (en) * | 2016-04-29 | 2016-09-21 | 杭州本构科技有限公司 | Constructing method and publishing system of complex interactive content |
| CN106407446A (en) * | 2016-09-29 | 2017-02-15 | 中科易研（北京）科技股份公司 | Network questionnaire establishment method and device |
| CN106407446B (en) * | 2016-09-29 | 2020-02-21 | 中科易研(北京)科技有限公司 | Network questionnaire construction method and device |
| CN106933790B (en) * | 2017-03-02 | 2020-07-07 | 重庆砖家宝网络科技发展有限公司 | Contract template manufacturing method and system |
| CN106933790A (en) * | 2017-03-02 | 2017-07-07 | 重庆砖家宝网络科技发展有限公司 | Contract template preparation method and system |
| CN107169274A (en) * | 2017-05-05 | 2017-09-15 | 深圳市心智心理测量技术研究所有限公司 | A Si Burger syndrome questionary handles method, device, equipment and storage medium |
| CN108229805A (en) * | 2017-12-27 | 2018-06-29 | 苏州工业园区报关有限公司 | Rule dynamic evaluating system and its method are closed in trade |
| CN108074140A (en) * | 2018-02-09 | 2018-05-25 | 弘成科技发展有限公司 | Intelligent Questionnaire systems and collecting method |
| CN109003117A (en) * | 2018-06-14 | 2018-12-14 | 万翼科技有限公司 | Generation method, device and the computer readable storage medium of questionnaire |
| CN109087129A (en) * | 2018-07-11 | 2018-12-25 | 万翼科技有限公司 | User's evaluation method, apparatus and computer readable storage medium |
| CN109087129B (en) * | 2018-07-11 | 2021-02-09 | 万翼科技有限公司 | User evaluation method, device and computer-readable storage medium |
| CN109165276A (en) * | 2018-08-22 | 2019-01-08 | 云图元睿（上海）科技有限公司 | Self-service questionnaire logic design method and system based on natural language |
| CN109344246A (en) * | 2018-09-25 | 2019-02-15 | 平安科技（深圳）有限公司 | A kind of electric questionnaire generation method, computer readable storage medium and terminal device |
| CN109344246B (en) * | 2018-09-25 | 2024-01-05 | 平安科技（深圳）有限公司 | Electronic questionnaire generating method, computer readable storage medium and terminal device |
| CN109598553A (en) * | 2018-12-04 | 2019-04-09 | 云图元睿（上海）科技有限公司 | Questionnaire intelligently tears conjunction method and system open |
| CN111370082B (en) * | 2018-12-26 | 2023-11-03 | 医渡云（北京）技术有限公司 | Question hiding display processing method and device, electronic equipment and readable medium |
| CN111370082A (en) * | 2018-12-26 | 2020-07-03 | 医渡云（北京）技术有限公司 | Question hiding display processing method and device, electronic equipment and readable medium |
| CN111460768B (en) * | 2019-01-02 | 2023-05-09 | 中国移动通信有限公司研究院 | Questionnaire processing method and device, electronic equipment and storage medium |
| CN111460768A (en) * | 2019-01-02 | 2020-07-28 | 中国移动通信有限公司研究院 | Questionnaire processing method and device, electronic equipment and storage medium |
| CN110362791A (en) * | 2019-02-22 | 2019-10-22 | 裴信 | Processing method, device and the computer readable storage medium of logic relevant issues |
| CN110189802B (en) * | 2019-04-28 | 2023-05-02 | 万达信息股份有限公司 | Bidirectional mapping queue research information system based on index storage model |
| CN110189802A (en) * | 2019-04-28 | 2019-08-30 | 万达信息股份有限公司 | Biaxial stress structure cohort study information system based on index storage model |
| CN110517021A (en) * | 2019-08-27 | 2019-11-29 | 出门问问信息科技有限公司 | A kind of data processing method, device, storage medium and electronic equipment |
| CN110752037A (en) * | 2019-10-23 | 2020-02-04 | 泰康保险集团股份有限公司 | Evaluation method and device, computer readable medium and electronic device |
| CN111369290A (en) * | 2020-03-05 | 2020-07-03 | 广州快决测信息科技有限公司 | Method and system for automatically generating data acquisition module |
| US12045251B2 (en) | 2020-03-05 | 2024-07-23 | Guangzhou Quick Decision Iinformation Technology Co., Ltd. | Method and system for automatically generating data acquisition module |
| CN113033158A (en) * | 2021-03-20 | 2021-06-25 | 广州快决测信息科技有限公司 | Questionnaire display method, device and storage medium |
| CN113033158B (en) * | 2021-03-20 | 2022-01-18 | 广州快决测信息科技有限公司 | Questionnaire display method, device and storage medium |
| CN113674032A (en) * | 2021-08-30 | 2021-11-19 | 广州快决测信息科技有限公司 | Net recommendation value questionnaire model building system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103810150A (en) | Automatic relation nestable questionnaire generating method and device | |
| Sinar | Data visualization | |
| CN110674295A (en) | Data labeling system based on deep learning | |
| CN107291795A (en) | A kind of dynamic word insertion of combination and the file classification method of part-of-speech tagging | |
| CN105975466A (en) | Method and device for machine manuscript writing aiming at short newsflashes | |
| CN105160023B (en) | Graphical data query processing method based on cloud server | |
| CN105095319A (en) | Time serialization based document identifying, associating, searching and showing system | |
| CN106484904A (en) | Patent retrieval analysis system and its analysis method | |
| CN109033277A (en) | Class brain system, method, equipment and storage medium based on machine learning | |
| CN103577556A (en) | Device and method for obtaining association degree of question and answer pair | |
| CN105431886A (en) | Rendering hierarchical visualizations of data sets | |
| CN103136305A (en) | Processing method and device used for test resource | |
| Piasecki et al. | WordNetLoom: a WordNet development system integrating form-based and graph-based perspectives | |
| CN105868289B (en) | A kind of multimedia courseware generation method and device | |
| CN106780656A (en) | Chart output intent and device | |
| CN109101519A (en) | Information acquisition system and Heterogeneous Information emerging system | |
| Silva et al. | Integrating big data into the computing curricula | |
| CN108470071A (en) | A kind of data processing method and device | |
| US20140039875A1 (en) | Visual analysis of phrase extraction from a content stream | |
| CN108733635A (en) | A kind of text message methods of exhibiting and device | |
| CN107563394A (en) | A kind of method and system of predicted pictures popularity | |
| CN107944723A (en) | A kind of " Tujia " picture weaving in silk cultural resource classification annotation method and system based on body | |
| CN103853775A (en) | Method for converting data storage format based on multimedia data | |
| CN110275938A (en) | Knowledge extraction method and system based on non-structured document | |
| JP2022070239A (en) | Visualization of general problem-solving idea and intelligent guidance method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20140521 |